Dental health status of professional football players during the Qatar 2023 AFC Asian Cup: a preliminary study
Bibliographic record
Abstract
Oral health is increasingly recognized as an important factor influencing athletic performance and overall quality of life; however, limited data are available on elite football players in Asia. This cross-sectional clinical study evaluated the oral health status of professional players participating in the Asian Football Cup held in Qatar between January and February 2023. Three calibrated dentists conducted standardized clinical examinations on 70 randomly selected players (mean age: 26.8 years) using the DMFT, BPE, and BEWE indices to assess dental caries, periodontal health, erosive tooth wear, wisdom teeth status, trauma, and temporomandibular joint (TMJ) disorders. Dental caries was present in 85.7% of players (mean DED = 5.6), and 77.1% had restorations. Gingivitis affected 82.9%, while 12.9% showed signs of periodontitis. Tooth erosion was detected in 88.6%, with 10% classified as high risk. Partially erupted wisdom teeth were identified in 38.6%, pericoronitis in 7.1%, sports-related trauma in 30%, and TMJ disorders in 21.4%. These findings highlight a substantial oral disease burden and support integrating preventive dental care into routine athlete health programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".